Weather Model Development for Aviation Stan Benjamin and Steve Weygandt: Assimilation and Modeling Branch, Chief/Deputy NOAA Earth System Research Laboratory, Global Systems Division, Boulder, CO Stan/Steve: Lead/Expert Model Development and Enhancement Product Development Team, AWRP/FAA 12h NOAA HRRR model forecast Valid 03z NOAA/ESRL/GSD 30 Oct 2013 1 Aviation Model Development Observed radar 03z June 30 2012
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Weather Model Development for Aviation Stan Benjamin and Steve Weygandt: Assimilation and Modeling Branch, Chief/Deputy NOAA Earth System Research Laboratory,
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Weather Model Development for Aviation Stan Benjamin and Steve Weygandt: Assimilation and Modeling Branch, Chief/Deputy NOAA Earth System Research Laboratory, Global Systems Division, Boulder, CO Stan/Steve: Lead/Expert Model Development and Enhancement Product Development Team, AWRP/FAA
12h NOAA HRRR model forecastValid 03z
NOAA/ESRL/GSD 30 Oct 2013 1Aviation Model Development
Observed radar03z June 30 2012
An Important Pinpoint Prediction Challenge: The 29 June 2012 Mid-Atlantic Derecho
Observations assimilated in hourly updated models (Rapid Refresh)- All used to initialize 3km HRRR
Radar reflectivity
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HRRR (and RAP) Future MilestonesHRRR MilestonesHigh Resolution – Why do we need it?
RAP
HRRR
Thunderstorm
~3km horizontal resolution needed to “resolve” thunderstorms
HRRR (and RAP) Future MilestonesHRRR MilestonesHigh Resolution – Why do we need it?
RAP
HRRR
Thunderstorm
~3km horizontal resolution needed to “resolve” thunderstorms
~3km horizontal resolution needed to “resolve” thunderstorms
…but 4x resolution costs 64x computer power
13-km 6hr forecast HRRR 6hr forecast
13-kmResolution
ParameterizedConvection
3-kmResolution
ExplicitConvection
5 PM EDTobserved
07 June 2012NO
STORM STRUCTURE
NO ESTIMATE OFSTORM
PERMEABILITY
ACCURATESTORM
STRUCTURE
ACCURATE ESTIMATEOF STORM
PERMABILITY
HRRR (and RAP) Future MilestonesHRRR Milestones3-km HRRR – what it gets you...
Radar Obs06:00z
18 May 201305z + 1 hour
Radar data assimilation: Getting storms in the right places
1-hr fcstradar DA(13-km and 3-
km)
1-hr fcstNO radar DA
• Run model backwards in time (reversible processes only)• Run model forward in time (heating from radar observations) • Digital filter after backward and forward step
Forward integration,full physics with obs-based latent heating
-20 min -10 min Initial +10 min + 20 min
RAP / HRRR model forecast
Backwards integration, no physics
Initial fields with improved balance, storm-scale circulation
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Radar data assimilation: How it works for RAP and HRRR
NO backward step or filter
for HRRR
00z init00z 12 Aug
2011
Convergence Cross-Section
RAPHRRR
RADAR
RAPHRRR
no radar
Rapid convective spin-up with radar data
Radar data assimilation: How it works for RAP
Reflectivity
+1 hr fcst01z 12 Aug
2011
Convergence Cross-Section
RAPHRRR
RADAR
RAPHRRR
no radar
Rapid convective spin-up with radar data
Radar data assimilation: How it works for RAP
Reflectivity
Cloud and Hydrometeor Analysis
Hydrometeor designation from radar
Adjust cycled explicit cloud fields using METAR and
satellite data
YES HM
29th Conf on EIPT (IIPS) 08 January 2013High-Resolution Rapid Refresh 20NOAA/ESRL/GSD 30 Oct 2013 20Aviation Model Development
Observations
Data Assimilation Cycle
Rapid cyclingNWP
Data Assimilation and Rapid Cycling Numerical Weather Prediction (model)
Required for improved weather guidance for:• Turbulence• Ceiling/visibility• Convective weather• Icing• Terminal/enroute weatherSafety and efficiency
Aviation hazard forecasts – all based on RAP and HRRR models (out to 15-18h)
Hourly updated 13km Rapid Refresh model forecasts
(development supported by FAA/MDE, NOAA)
Refreshing from latest observations every hour gives better accuracy
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Subset of full domain
An example of computations needed1800x1059x50 grid points = 95 E6 grid pointsx 50,000 floating pt ops per grid point = 4.75 E12 FPA / time stepx 2160 time steps / 12h forecast = 10 E15 FPA / 12h forecast
10,000,000,000,000,000 calculations for one12h HRRR CONUS forecast
Weather computer model: Solving physics equations on many points repeatedly to provide 3-D forecast forecast of temperature, wind, moisture, clouds, etc.
1800 points
1060 points
Model Version Initialized Forecast Length Run Time # CPUs Disk Space